Crop models are essential in undertaking large scale estimation of crop production of diverse crop species, especially in assessing food availability and climate change impacts. In this study, an existing model (SSM, Simple Simulation Models) was adapted to simulate a large number of plant species including orchard species and perennial forages. Simplification of some methods employed in the original model was necessary to deal with limited data availability for some of the plant species to be simulated. The model requires limited, readily available input information. The simulations account for plant phenology, leaf area development and senescence, dry matter accumulation, yield formation, and soil water balance in a daily time step. Parameterization of the model for new crops/cultivars is easy and straight-forward. The resultant model (SSM-iCrop2) was parameterized and tested for more than 30 crop species of Iran using numerous field experiments. Tests showed the model was robust in the predictions of crop yield and water use. Root mean square of error as percentage of observed mean for yield was 18% for grain field crops, 14% for non-grain crops 14% for vegetables and 28% for fruit trees.

中文翻译：

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SSM-iCrop2：大面积不同作物物种的简单模型

作物模型对于对不同作物物种的作物产量进行大规模估计至关重要，尤其是在评估粮食供应和气候变化影响方面。在这项研究中，现有模型（SSM，Simple Simulation Models）适用于模拟大量植物物种，包括果园物种和多年生牧草。需要对原始模型中采用的某些方法进行简化，以处理要模拟的某些植物物种的有限数据可用性。该模型需要有限的、现成的输入信息。模拟解释了植物物候、叶面积发育和衰老、干物质积累、产量形成和每日时间步长中的土壤水分平衡。新作物/栽培品种的模型参数化简单而直接。由此产生的模型 (SSM-iCrop2) 被参数化，并使用大量田间试验对伊朗 30 多种作物进行了测试。测试表明，该模型在作物产量和用水量的预测中是稳健的。谷物大田作物的均方根误差占观察到的平均产量的百分比为 18%，非粮作物为 14%，蔬菜为 14%，果树为 28%。